Crowdsourced geospatial data is reshaping urban sciences

被引:8
作者
Huang, Xiao [1 ]
Wang, Siqin [2 ]
Lu, Tianjun [3 ]
Liu, Yisi [3 ]
Serrano-Estrada, Leticia [4 ]
机构
[1] Emory Univ, Dept Environm Sci, Atlanta, GA 30322 USA
[2] Univ Southern Calif, Spatial Sci Inst, Los Angeles, CA USA
[3] Univ Kentucky, Dept Epidemiol & Environm Hlth, Lexington, KY USA
[4] Univ Alicante, Bldg Sci & Urbanism Dept, Alicante, Spain
关键词
Crowdsourced geospatial data; Urban science; Big data; Urban informatics;
D O I
10.1016/j.jag.2024.103687
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
For many years, urban sciences relied heavily on traditional, authoritative data sources. However, a paradigm shift has occurred recently with the advent of citizen-driven data contribution. This evolution in data acquisition for urban science is attributable to advancements in positioning and navigation technologies, widespread use of digital devices, the rise of Web 2.0, enhanced broadband communications, and refined data management techniques. The significance of crowdsourced geospatial data in the realm of urban sciences is now widely acknowledged. A diverse array of novel data sources has been increasingly gaining prominence. These include, but are not limited to, social media platforms, street view images, GPS signals from cellphones, smartphone applications, transaction records, online mapping services, and sensor data crowdsourced from the public. This innovative approach to data collection, fuelled by contributions from individuals around the globe, opens up possibilities for accessing data that would otherwise remain untapped. This editorial aims to offer a unique perspective on how crowdsourced geospatial data is revolutionizing the field of urban sciences. We categorize the studies into five primary areas: 1) tourism, 2) urban visuals and perception, 3) urban infrastructure and functionality, 4) mobility and transportation, and 5) miscellaneous, and further discuss the challenges and opportunities that lie ahead for future research in this field.
引用
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页数:5
相关论文
共 23 条
[1]   Sensitivity of measuring the urban form and greenery using street-level imagery: A comparative study of approaches and visual perspectives [J].
Biljecki, Filip ;
Zhao, Tianhong ;
Liang, Xiucheng ;
Hou, Yujun .
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2023, 122
[2]   Analysing gender differences in the perceived safety from street view imagery [J].
Cui, Qinyu ;
Zhang, Yan ;
Yang, Guang ;
Huang, Yiting ;
Chen, Yu .
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2023, 124
[3]   Assessing spatiotemporal bikeability using multi-source geospatial big data: A case study of Xiamen, China [J].
Dai, Shaoqing ;
Zhao, Wufan ;
Wang, Yanwen ;
Huang, Xiao ;
Chen, Zhidong ;
Lei, Jinghan ;
Stein, Alfred ;
Jia, Peng .
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2023, 125
[4]   Agent-based modelling with geographically weighted calibration for intra-urban activities simulation using taxi GPS trajectories [J].
Gong, Shuhui ;
Dong, Xiangrui ;
Wang, Kaiqi ;
Lei, Bingli ;
Jia, Zizhao ;
Qin, Jiaxin ;
Roadknight, Chris ;
Liu, Yu ;
Cao, Rui .
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2023, 122
[5]   Self-supervised audiovisual representation learning for remote sensing data [J].
Heidler, Konrad ;
Mou, Lichao ;
Hu, Di ;
Jin, Pu ;
Li, Guangyao ;
Gan, Chuang ;
Wen, Ji-Rong ;
Zhu, Xiao Xiang .
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2023, 116
[6]   Learning visual overlapping image pairs for SfM via CNN fine-tuning with photogrammetric geometry information [J].
Hou, Qianbao ;
Xia, Rui ;
Zhang, Jiahuan ;
Feng, Yu ;
Zhan, Zongqian ;
Wang, Xin .
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2023, 116
[7]   A comprehensive framework for evaluating the quality of street view imagery [J].
Hou, Yujun ;
Biljecki, Filip .
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2022, 115
[8]  
Huang X., 2024, J. Remote Sensing, DOI [10.34133/remotesensing.010, DOI 10.34133/REMOTESENSING.010]
[9]  
Huang X, 2023, Geoinformatics Geosci, P109
[10]   Land cover mapping via crowdsourced multi-directional views: The more directional views, the better [J].
Huang, Xiao ;
Yang, Di ;
He, Yaqian ;
Nelson, Peder ;
Low, Russanne ;
McBride, Shawna ;
Mitchell, Jessica ;
Guarraia, Michael .
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2023, 122